FPGA acceleration of tensor network computing for quantum spin models

Yang Liang; Songtai Lv; Zhexuan Tang; Liguo Zhou; Qibin Zheng; Haiyuan Zou

doi:10.1063/5.0239473

FPGA acceleration of tensor network computing for quantum spin models

Rev Sci Instrum. 2025 Jan 1;96(1):013903. doi: 10.1063/5.0239473.

Authors

Yang Liang¹, Songtai Lv², Zhexuan Tang¹, Liguo Zhou¹, Qibin Zheng¹, Haiyuan Zou²

Affiliations

¹ Quantum Medical Sensing Laboratory, School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
² Key Laboratory of Polar Materials and Devices (MOE), School of Physics and Electronic Science, East China Normal University, Shanghai 200241, China.

PMID: 39846808
DOI: 10.1063/5.0239473

Abstract

Increasing the degree of freedom for quantum entanglement within tensor networks can enhance the depiction of the essence in many-body systems. However, this enhancement comes with a significant increase in computational complexity and critical slowing down, which drastically increases time consumption. This work converts a quantum tensor network algorithm into a classical circuit on the Field Programmable Gate Arrays (FPGAs) and arranges the computing unit with a dense parallel design, efficiently optimizing the time consumption. Test results show that the FPGA-based design achieves a computational speed 1.7 times greater than that of the central processing unit and is comparable to the graphics processing unit. This work explores a scalable and reusable approach suitable for parallel tensor operations implemented on FPGA, advancing research in quantum physics for many-body computing and quantum technologies.